When we think of an object in a supervised learn- ing setting, we usually perceive it as a collection of fixed at- tribute values. Although this setting may be suited well for many classification tasks, we propose a n...When we think of an object in a supervised learn- ing setting, we usually perceive it as a collection of fixed at- tribute values. Although this setting may be suited well for many classification tasks, we propose a new object repre- sentation and therewith a new challenge in data mining; an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different levels of quality. Obtaining a more detailed representation of an ob- ject comes with a cost. This raises the interesting question of which objects we want to enhance under a given budget and cost model. This new setting is very useful whenever re- sources like computing power, memory or time are limited. We propose a new active adaptive algorithm （AAA） to im- prove objects in an iterative fashion. We demonstrate how to create a hierarchical object representation and prove the ef- fectiveness of our new selection algorithm on these datasets.展开更多
文摘When we think of an object in a supervised learn- ing setting, we usually perceive it as a collection of fixed at- tribute values. Although this setting may be suited well for many classification tasks, we propose a new object repre- sentation and therewith a new challenge in data mining; an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different levels of quality. Obtaining a more detailed representation of an ob- ject comes with a cost. This raises the interesting question of which objects we want to enhance under a given budget and cost model. This new setting is very useful whenever re- sources like computing power, memory or time are limited. We propose a new active adaptive algorithm （AAA） to im- prove objects in an iterative fashion. We demonstrate how to create a hierarchical object representation and prove the ef- fectiveness of our new selection algorithm on these datasets.
